Psychometric properties of Gaudiebility Scale (Modulators of Enjoyment) for Children and Adolescents (GSCA)
Bibliographic record
Abstract
Abstract The decrease in the ability to enjoy can be considered a risk factor or marker of mental disorders. Therefore, it can be useful to have a scale to quantify gaudiebility in children and adolescents. The objective of the present work was to build and analyze the psychometric properties of the Gaudiebility Scale for Children and Adolescents (GSCA). 1,264 primary, secondary and high school students responded to GSCA, Rosenberg's self-esteem scale, Positive and Negative Affect schedule, Center of Epidemiological Studies of Depression scale. Through a confirmatory factor analysis using WLSMV (Weighted Least Square) estimation with a reparameterization with a value of δ = .05 and forcing a non-orthogonal factor structure was observed to 5 factors model (Enjoyment in Company, Self-efficacy versus boredom, Sense of humor, Imagination and Interest) related, results (χ2/df = 9.40; CFI = .931; TLI = .946) indicate that the modification indices did not present relevant values, so it would not be pertinent to propose any other alternative model. In addition, an appropriate internal reliability (Cronbach α = .794) was observed in the total scale and in the 5 subscales. Finally, an adequate evidence of validity was observed. It is concluded that GSCA seems appropriate to quantify gaudiebility levels in children and adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".